{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "import pandas as pd\n",
    "from yaml import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "df=pd.read_csv('ec_data.csv',encoding='iso-8859-1')\n",
    "df.head()"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "print(df.head())\n",
    "print(df.describe().round(2))\n",
    "print(df.info())\n",
    "print(df.shape)"
   ],
   "id": "f54de6807805fd84",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "df.isnull().sum().sort_values(ascending=False)",
   "id": "aaa0dbe8c34949db"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "df_ec=df.dropna()\n",
    "df_ec=df_ec[df_ec.Quantiny>0]\n"
   ],
   "id": "bc00cc045529cc02"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
